1. Field of the Invention
The present invention is a system and method for automatically changing the advertisement display contents in real-time based on a customer's behavior or a group of customers' behavior in a retail store by analyzing the behavioral pattern of the customer or the group of customers, based on visual information from the shopping and walkthrough history of said customer or said group of customers, using arrays of image capturing devices and a plurality of computer vision technologies on the visual information.
2. Background of the Invention
There have been earlier attempts to help customers and sales persons in a shopping process utilizing computer-aided devices, such as U.S. Pat. No. 5,047,614 of Bianco, U.S. Pat. No. 5,283,731 of Lalonde, et al. (hereinafter Lalonde), and U.S. Pat. No. 5,309,355 of Lockwood. Bianco disclosed a portable and remote bar code reading means. Lalonde disclosed a computer-based classified advertisement system. Lockwood disclosed an automated sales system, which enhances a travel agent's marketing ability, especially with regard to computerized airline reservation systems.
There have also been attempts to customize and distribute targeted advertising content to customers based on customer profiles, purchase history, or demographic information from the customer in the prior art.
U.S. Pat. No. 5,155,591 of Wachob and U.S. Pat. No. 5,636,346 of Saxe disclosed methods and systems for delivering targeted advertisements and programming to demographically targeted television audiences. U.S. Pat. No. 6,002,393 of Hite, et al. disclosed a system and method for delivering targeted TV advertisements to customers utilizing controllers.
U.S. Pat. No. 5,459,306 of Stein, et al. (hereinafter Stein) disclosed a method and system for delivering product picks to a prospective individual user, especially with regard to a movie rental and sale business. Stein gathered the user information and the user's usage information, which are correlated with a user code and classified based on the use of at least one product. The product picks (promotions and recommendations) are delivered based on the classified information and the user information. However, Stein is foreign to the automatic method of gathering the user information, especially the user behavior, in real-time in a store.
U.S. Pat. No. 6,119,098 of Guyot, et al. (hereinafter Guyot) disclosed a method and apparatus for targeting and distributing advertisements over a distributed network, such as the Internet, to the subscriber's computer. The targeted advertisements were based on a personal profile provided by the subscriber. Guyot was primarily intended for the subscriber with a computer at home, not at a store or a public retail place, and the targeted advertisement creation relied on the non-automatic response from the customer.
U.S. Pat. No. 6,182,050 of Ballard disclosed a method and apparatus for distributing advertisements online using target criteria screening, which also provided a method for maintaining end user privacy. In the disclosure, the demographic information or a desired affinity ranking was gathered by the end user, who completed a demographic questionnaire and ranked various categories of products and services. Ballard is foreign to the behavior analysis of customers in a retail store.
U.S. Pat. No. 6,055,573 of Gardenswartz, et al. and its continuation U.S. Pat. No. 6,298,330 of Gardenswartz, et al. (hereinafter Gardenswartz) disclosed a method and apparatus for communicating with a computer in a network based on the offline purchase history of a particular customer. Gardenswartz included the delivery of a promotional incentive for a customer to comply with a particular behavioral pattern. In Gardenswartz, the customer supplied the registration server with information about the customer, including demographics of the customer, to generate an online profile. In Gardenswartz, the content of advertisements were selected based on changes in the customers' purchase history behaviors, and Gardenswartz is foreign to the real-time behavioral pattern analysis in a retail store, such as the shopping path analysis of the customers in the retail store.
U.S. Pat. No. 6,385,592 of Angles, et al. (hereinafter Angles) disclosed a method and apparatus for delivering customized advertisements within interactive communication systems. In Angles, the interactive devices include computers connected to online services, interactive kiosks, interactive television systems and the like. In Angles, the advertising provider computer generated a customized advertisement based on the customer's profile, upon receiving the advertising request. In Angles, the customer, who wished to receive customized advertisement, first registered with the advertisement provider by entering the demographic information into the advertisement provider's demographic database. Therefore Angles is foreign to the automatic narrowcasting of the advertisement in a retail space based on customer behavior in real-time, without requiring any cumbersome response from the customer.
U.S. Pat. No. 6,408,278 of Carney, et al. (hereinafter Carney) disclosed a method and apparatus for delivering programming content on a network of electronic out-of-home display devices. In Carney, the network includes a plurality of display devices located in public places, and the delivered programming content is changed according to the demographics of the people. Carney also suggests demographic data gathering devices, such as kiosk and automatic teller machines.
U.S. Pat. No. 6,484,148 of Boyd (hereinafter Boyd) disclosed electronic advertising devices and methods for providing targeted advertisements based on the customer profiles. Boyd included a receiver for receiving identifying signals from individuals such as signals emitted by cellular telephones, and the identifying signal was used for the targeted advertisements to be delivered to the individuals.
U.S. Pat. No. 6,847,969 of Mathai, et al. (hereinafter Mathai) disclosed a method and system for providing personalized advertisements to customers in a public place. In Mathai, the customer inserts a personal system access card into a slot on a terminal, which automatically updates the customer profile based on the customer's usage history. The customer profile is used for targeted advertising in Mathai. However, the usage of a system access card is cumbersome to the customer. The customer has to carry around the card when shopping, and the method and apparatus is not usable if the card is lost or stolen. Clearly, Mathai is foreign to the idea of changing the advertising content in a retail store based on the real-time analysis of the customer's behavior inside the store utilizing non-cumbersome automatic computer vision technology.
Haritaoglu, et al. (hereinafter Haritaoglu) in “Attentive Billboards”, 11th International Conference on Image Analysis and Processing, Sep. 26-28, 2001, Palermo, Italy, disclosed a real-time vision system, which detected, tracked, and counted the number of people standing in front of billboards. Especially, Haritaoglu disclosed an infrared illumination based pupil detection to determine whether the people are looking at the billboards.
However, as Haritaoglu disclosed, the short-range requirement for the infrared illumination-based pupil detection technology makes the method impractical in the retail store environment. In Haritaoglu, the people have to be close to the billboard within a 10-foot distance. Since it is not practical to force the customers to stand within a 10-foot distance from the displayed object, the method of using an infrared light source will miss many viewers who are outside the range but within the opportunity to see (OTS) area in the vicinity of the displayed object. In addition, in order to reliably detect the bright eye on-axis illumination from one of the infrared light sources, which is closely located to the camera, the size of the images has to be large, which can also be impractical. If the size of the images has to be relatively large, it is difficult for the camera to cover the multiple viewers in the OTS area, while focusing on a couple of viewers' faces. Furthermore, the additional infrared devices increase the cost per displayed object, and it will be a difficult task to install the devices in a manner non-obtrusive to the customers. Therefore, it is necessary to have a non-obtrusive, cost-efficient, and broad-range means for tracking customers.
In another part of the effort in a retail shopping environment, there have also been attempts for collecting market research data in a retail store.
U.S. Pat. No. 5,331,544 of Lu, et al. (hereinafter Lu) disclosed an automated system for collecting market research data. Especially, Lu disclosed “an automatic face recognition system and method to identify a retail customer,” which can measure the shopping frequency at a given store. Lu also disclosed “a shopper's attentiveness to a display or advertisement may be correlated with purchases of products and with other demographic purchase-related variables.” However, Lu is foreign to narrowcasting or behavior analysis based on customer movement in a retail store.
U.S. Pat. No. 6,236,975 of Boe, et al. (hereinafter Boe) disclosed a method and system for compiling customer data using an online interaction between a customer and a survey system. Boe's system is intended for targeted marketing, but it is not an, automatic system. The need for an automatic system, which does not require any involvement from the customers, for delivering targeted advertisement content to a display in a retail store based on real-time analysis of the customers' behavior is foreign to Boe.
U.S. Pat. No. 6,925,441 of Jones, III, et al. (hereinafter Jones) disclosed a system and method for targeted marketing, in which the targeted marketing is based on the financial characteristics of the customer, the type of offer being made, and the channel of communication for delivery of the offer. One of the objects in Jones is to have a better description of a customer's spending habits through querying databases. However, Jones is foreign to the idea of producing targeted advertisement based on real-time behavior analysis of customers in a retail store.
U.S. Pat. No. 7,003,476 of Samra, et al. (hereinafter Samra) also disclosed a system and method for targeted marketing using a ‘targeting engine’, which analyzes data input and generates data output. Samra used historical data to determine a target group based on a plurality of embedded models, where the models are defined as predicted customer profiles based on historical data, and the models are embedded in the ‘targeting engine’. In Samra, the ‘targeting engine’ maintains a customer database based on demographics, but Samra is clearly foreign to the idea of displaying targeted advertisement content in a retail store based on the automatic and real-time behavior analysis of the customer.
With regard to the behavior analysis, an exemplary disclosure can be found in U.S. Pat. No. 6,582,380 of Kazlausky, et al. (hereinafter Kazlausky), which disclosed a system and method for monitoring the activity level of children in a classroom environment. Clearly, Kazlausky is foreign to the concept of analyzing the customers'behavior in real-time, based on visual information of the customers, such as the shopping path tracking and analysis, for the sake of delivering targeted advertisement content to a display in a retail store.
U.S. Pat. No. 6,741,973 of Dove, et al. (hereinafter Dove) disclosed a model of generating customer behavior in a transaction environment. Although Dove disclosed video cameras in a real bank branch as a way to observe the human behavior, Dove is clearly foreign to the concept of automatic and real-time analysis of the customers' behavior, based on visual information of the customers in a retail environment, such as the shopping path tracking and analysis, for the sake of delivering targeted advertisement content to a display in the retail environment.
There have been earlier attempts for understanding customers' shopping behavior captured in a video in a targeted environment, such as in a retail store, using cameras.
U.S. Pat. Appl. Pub. No. 2006/0010028 of Sorensen (hereinafter Sorensen 1) disclosed a method for tracking shopper movements and behavior in a shopping environment using a video. In Sorensen 1, a user indicated a series of screen locations in a display at which the shopper appeared in the video, and the series of screen locations were translated to store map coordinates. The step of receiving the user input via input devices, such as a pointing device or keyboard, makes Sorensen 1 inefficient for handling a large amount of video data in a large shopping environment with a relatively complicated store layout, especially over a long period of time. The manual input by a human operator/user cannot efficiently track all of the shoppers in such cases, not to mention the possible human errors due to tiredness and boredom. Additionally, the manual input approach is not scalable when the number of shopping environments to handle increases.
Although U.S. Pat. Appl. Pub. No. 2002/0178085 of Sorensen (hereinafter Sorensen 2) disclosed a usage of tracking device and store sensors in a plurality of tracking systems primarily based on the wireless technology, such as the RFID, Sorensen 2 is clearly foreign to the concept of applying computer vision-based tracking algorithms to the field of understanding customers' shopping behavior and movement. In Sorensen 2, each transmitter was typically attached to a hand-held or push-type cart. Therefore, Sorensen 2 cannot distinguish the behaviors of multiple shoppers using one cart from a single shopper who is also using also one cart. Although Sorensen 2 disclosed that the transmitter may be attached directly to a shopper via a clip or other form of customer surrogate when a customer is shopping without a cart, this will not be practical due to the additionally introduced cumbersome steps to the shopper, not to mention the inefficiency of managing the transmitter for each individual shopper.
With regard to the temporal behavior of customers, U.S. Pat. Appl. Pub. No. 2003/0002712 of Steenburgh, et al. (hereinafter Steenburgh) disclosed a relevant prior art. Steenburgh disclosed a method for measuring dwell time of an object, particularly a customer in a retail store, which enters and exits an environment, by tracking the object and matching the entry signature of the object to the exit signature of the object, in order to find out how long people spend in retail stores.
U.S. Pat. Appl. Pub. No. 2003/0053659 of Pavlidis, et al. (hereinafter Pavlidis) disclosed a method for moving object assessment, including an object path of one or more moving objects in a search area, using a plurality of imaging devices and segmentation by background subtraction. In Pavlidis, the object included customers. Pavlidis was primarily related to monitoring a search area for surveillance, but Pavlidis also included itinerary statistics of customers in a department store.
U.S. Pat. Appl. Pub. No. 2004/0120581 of Ozer, et al. (hereinafter Ozer) disclosed a method for identifying activity of customers for marketing purpose or activity of objects in surveillance area, by comparing the detected objects with the graphs from a database. Ozer tracked the movement of different object parts and combined them to high-level activity semantics, using several Hidden Markov Models (HMMs) and a distance classifier.
U.S. Pat. Appl. Pub. No. 2004/0131254 of Liang, et al. (hereinafter Liang) also disclosed the Hidden Markov Models (HMMs) as a way, along with the rule-based label analysis and the token parsing procedure, to characterize behavior. Liang disclosed a method for monitoring and classifying actions of various objects in a video, using background subtraction for object detection and tracking. Liang is particularly related to animal behavior in a lab for testing drugs.
With regard to path analysis, an exemplary disclosure can be found in U.S. Pat. No. 6,584,401 of Kirshenbaum, et al. (hereinafter Kirshenbaum), which disclosed a method and apparatus for automatically gathering data on paths taken by commuters for the sake of improving the commute experience. Kirshenbaum disclosed a global positioning system, mobile phone, personal digital assistant, telephone, PC, and departure or arrival indications as some ways for gathering the commute data. Clearly, Kirshenbaum is foreign to the concept of analyzing the customers' behavior in real-time, based on visual information of the customers using the means for capturing images, such as the shopping path tracking and analysis, for the sake of delivering targeted advertisement content to a display in a retail store.
U.S. Pat. Appl. Pub. No. 2003/0058339 of Trajkovic, et al. (hereinafter Trajkovic) disclosed a method for detecting an event through repetitive patterns of human behavior. Trajkovic learned multidimensional feature data from the repetitive patterns of human behavior and computed a probability density function (PDF) from the data. Then, a method for the PDF analysis, such as Gaussian or clustering techniques, was used to identify the repetitive patterns of behavior and unusual behavior through the variance of the Gaussian distribution or cluster.
Although Trajkovic can model a repetitive behavior through the PDF analysis, Trajkovic is clearly foreign to the event detection for the aggregate of non-repetitive behaviors, such as the shopper traffic in a physical store. The shopping path of an individual shopper can be repetitive, but each shopping path in a group of aggregated shopping paths of multiple shoppers is not repetitive. Trajkovic did not disclose the challenges in the event detection based on customers' behaviors in a video in a retail environment such as this, and Trajkovic is clearly foreign to the challenges that can be found in a retail environment.
While the above mentioned prior arts try to deliver targeted contents to the customer in a computer network or a standalone system, using customer profiles, a customer's purchase history, or demographic information from customers, they are clearly foreign to the automatic and real-time delivery of targeted contents (narrowcasting) of the advertisement in a retail space based on customer behavior, such as the shopping path in the store, without requiring any cumbersome involvement from the customer.
Although there have been attempts to customize advertising content, using demographic information or a customer profile through cumbersome requests to the customers and responses from them, such as using questionnaires, registration forms, or electronic devices, in the prior art, the targeted advertisement based on automatic customer behavior analysis in a retail store using an efficient computer vision tracking technology has not been introduced in any of the prior art. Furthermore, automatic and real-time customer behavioral pattern analysis in retail stores based on the computer vision technology has been foreign to any of the prior art.
The present invention is a method and system for selectively executing targeted media on a means for displaying output based on the automatic and real-time analysis of the customer behavior, called behavior-based narrowcasting (BBN), in the view of the means for capturing images, providing an efficient and robust solution, which solves the aforementioned problems in the prior art.
Computer vision algorithms have been shown to be an effective means for detecting and tracking people. These algorithms also have been shown to be effective in analyzing the behavior of people in the view of the means for capturing images. This allows for the possibility of connecting the visual information from a scene to the behavior and content of advertising media. The invention allows freedom of installation position between data gathering devices, a set of cameras, and display devices. The invention automatically and unobtrusively analyzes the customer behavior without involving any hassle of feeding information manually by the customer.
Another limitation found in the prior arts is that the data gathering device is often collocated adjacent to the display device in the prior art. However, depending on the public place environment and the business goal, where the embodiment of the system is installed, it may be necessary to install the data gathering devices independent of the position of the display device. For example, some owners of public places could want to utilize the widely used and already installed surveillance cameras in their public places for the data gathering. In this situation, the surveillance cameras may not necessarily be collocated adjacent to the display devices.
The BBN enables the separation of the device locations, which makes the layout of equipment installation flexible. In the above exemplary cases, the BBN enables the targeted content to be delivered and displayed through display devices, which do not need to be collocated adjacent to the data gathering devices, such as cameras. In addition, the targeted message propagation in the BBN looks more coincidental than deliberately arranged when the customer arrives at the advertisement display at the end of the path.